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ORCIDAmir Globerson
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- affiliation: Hebrew University of Jerusalem, Israel
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2020 – today
- 2024
[j9]Roi Cohen, Eden Biran, Ori Yoran, Amir Globerson, Mor Geva:
Evaluating the Ripple Effects of Knowledge Editing in Language Models. Trans. Assoc. Comput. Linguistics 12: 283-298 (2024)- [c113]Maya Bechler-Speicher, Amir Globerson, Ran Gilad-Bachrach
:
TREE-G: Decision Trees Contesting Graph Neural Networks. AAAI 2024: 11032-11042 - [c112]Alberto Hojel, Yutong Bai, Trevor Darrell, Amir Globerson, Amir Bar:
Finding Visual Task Vectors. ECCV (43) 2024: 257-273 - [c111]Amir Bar, Florian Bordes, Assaf Shocher, Mido Assran, Pascal Vincent, Nicolas Ballas, Trevor Darrell, Amir Globerson, Yann LeCun:
Stochastic positional embeddings improve masked image modeling. ICML 2024 - [c110]Maya Bechler-Speicher, Ido Amos, Ran Gilad-Bachrach, Amir Globerson:
Graph Neural Networks Use Graphs When They Shouldn't. ICML 2024 - [c109]Noam Razin, Yotam Alexander, Edo Cohen-Karlik, Raja Giryes, Amir Globerson, Nadav Cohen:
Implicit Bias of Policy Gradient in Linear Quadratic Control: Extrapolation to Unseen Initial States. ICML 2024 - [c108]Roei Herzig, Ofir Abramovich, Elad Ben-Avraham, Assaf Arbelle, Leonid Karlinsky, Ariel Shamir, Trevor Darrell, Amir Globerson:
PromptonomyViT: Multi-Task Prompt Learning Improves Video Transformers using Synthetic Scene Data. WACV 2024: 6789-6801 - [i78]Noam Razin, Yotam Alexander, Edo Cohen-Karlik, Raja Giryes, Amir Globerson, Nadav Cohen:
Implicit Bias of Policy Gradient in Linear Quadratic Control: Extrapolation to Unseen Initial States. CoRR abs/2402.07875 (2024) - [i77]Alberto Hojel, Yutong Bai, Trevor Darrell, Amir Globerson, Amir Bar:
Finding Visual Task Vectors. CoRR abs/2404.05729 (2024) - [i76]Amir Bar, Arya Bakhtiar, Danny Tran, Antonio Loquercio, Jathushan Rajasegaran, Yann LeCun, Amir Globerson, Trevor Darrell:
EgoPet: Egomotion and Interaction Data from an Animal's Perspective. CoRR abs/2404.09991 (2024) - [i75]R. Alex Hofer, Joshua Maynez, Bhuwan Dhingra, Adam Fisch, Amir Globerson, William W. Cohen:
Bayesian Prediction-Powered Inference. CoRR abs/2405.06034 (2024) - [i74]Maya Bechler-Speicher, Amir Globerson, Ran Gilad-Bachrach:
The Intelligible and Effective Graph Neural Additive Networks. CoRR abs/2406.01317 (2024) - [i73]Avi Caciularu, Alon Jacovi, Eyal Ben-David, Sasha Goldshtein, Tal Schuster, Jonathan Herzig, Gal Elidan, Amir Globerson:
TACT: Advancing Complex Aggregative Reasoning with Information Extraction Tools. CoRR abs/2406.03618 (2024) - [i72]Adam Fisch, Joshua Maynez, R. Alex Hofer, Bhuwan Dhingra, Amir Globerson, William W. Cohen:
Stratified Prediction-Powered Inference for Hybrid Language Model Evaluation. CoRR abs/2406.04291 (2024) - [i71]Eden Biran, Daniela Gottesman, Sohee Yang, Mor Geva, Amir Globerson:
Hopping Too Late: Exploring the Limitations of Large Language Models on Multi-Hop Queries. CoRR abs/2406.12775 (2024) - [i70]Assaf Ben-Kish, Itamar Zimerman, Shady Abu-Hussein, Nadav Cohen, Amir Globerson, Lior Wolf, Raja Giryes:
DeciMamba: Exploring the Length Extrapolation Potential of Mamba. CoRR abs/2406.14528 (2024) - [i69]Naama Rozen, Gal Elidan, Amir Globerson, Ella Daniel:
Do LLMs have Consistent Values? CoRR abs/2407.12878 (2024) - [i68]Gilad Yehudai, Haim Kaplan, Asma Ghandeharioun, Mor Geva, Amir Globerson:
When Can Transformers Count to n? CoRR abs/2407.15160 (2024) - [i67]Nitzan Bitton Guetta, Aviv Slobodkin, Aviya Maimon, Eliya Habba, Royi Rassin, Yonatan Bitton, Idan Szpektor, Amir Globerson, Yuval Elovici:
Visual Riddles: a Commonsense and World Knowledge Challenge for Large Vision and Language Models. CoRR abs/2407.19474 (2024) - 2023
- [c107]Roni Rabin, Alexandre Djerbetian, Roee Engelberg, Lidan Hackmon, Gal Elidan, Reut Tsarfaty, Amir Globerson:
Covering Uncommon Ground: Gap-Focused Question Generation for Answer Assessment. ACL (2) 2023: 215-227 - [c106]Ori Ram, Liat Bezalel, Adi Zicher, Yonatan Belinkov, Jonathan Berant, Amir Globerson:
What Are You Token About? Dense Retrieval as Distributions Over the Vocabulary. ACL (1) 2023: 2481-2498 - [c105]Roi Cohen, Mor Geva, Jonathan Berant, Amir Globerson:
Crawling The Internal Knowledge-Base of Language Models. EACL (Findings) 2023: 1811-1824 - [c104]Roee Hendel, Mor Geva, Amir Globerson:
In-Context Learning Creates Task Vectors. EMNLP (Findings) 2023: 9318-9333 - [c103]Mor Geva, Jasmijn Bastings, Katja Filippova, Amir Globerson:
Dissecting Recall of Factual Associations in Auto-Regressive Language Models. EMNLP 2023: 12216-12235 - [c102]Roi Cohen, May Hamri, Mor Geva, Amir Globerson:
LM vs LM: Detecting Factual Errors via Cross Examination. EMNLP 2023: 12621-12640 - [c101]Roei Herzig, Alon Mendelson, Leonid Karlinsky, Assaf Arbelle, Rogério Feris, Trevor Darrell, Amir Globerson:
Incorporating Structured Representations into Pretrained Vision & Language Models Using Scene Graphs. EMNLP 2023: 14077-14098 - [c100]Edo Cohen-Karlik, Itamar Menuhin-Gruman, Raja Giryes, Nadav Cohen, Amir Globerson:
Learning Low Dimensional State Spaces with Overparameterized Recurrent Neural Nets. ICLR 2023 - [e3]Alice Oh, Tristan Naumann, Amir Globerson, Kate Saenko, Moritz Hardt, Sergey Levine:
Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023. 2023 [contents] - [i66]Roi Cohen, Mor Geva, Jonathan Berant, Amir Globerson:
Crawling the Internal Knowledge-Base of Language Models. CoRR abs/2301.12810 (2023) - [i65]Mor Geva, Jasmijn Bastings, Katja Filippova, Amir Globerson:
Dissecting Recall of Factual Associations in Auto-Regressive Language Models. CoRR abs/2304.14767 (2023) - [i64]Roei Herzig, Alon Mendelson, Leonid Karlinsky, Assaf Arbelle, Rogério Feris, Trevor Darrell, Amir Globerson:
Incorporating Structured Representations into Pretrained Vision & Language Models Using Scene Graphs. CoRR abs/2305.06343 (2023) - [i63]Roi Cohen, May Hamri, Mor Geva, Amir Globerson:
LM vs LM: Detecting Factual Errors via Cross Examination. CoRR abs/2305.13281 (2023) - [i62]Roni Rabin, Alexandre Djerbetian, Roee Engelberg, Lidan Hackmon, Gal Elidan, Reut Tsarfaty, Amir Globerson:
Covering Uncommon Ground: Gap-Focused Question Generation for Answer Assessment. CoRR abs/2307.03319 (2023) - [i61]Roi Cohen, Eden Biran, Ori Yoran, Amir Globerson, Mor Geva:
Evaluating the Ripple Effects of Knowledge Editing in Language Models. CoRR abs/2307.12976 (2023) - [i60]Amir Bar, Florian Bordes, Assaf Shocher, Mahmoud Assran, Pascal Vincent, Nicolas Ballas, Trevor Darrell, Amir Globerson, Yann LeCun:
Predicting masked tokens in stochastic locations improves masked image modeling. CoRR abs/2308.00566 (2023) - [i59]Maya Bechler-Speicher, Ido Amos, Ran Gilad-Bachrach, Amir Globerson:
Graph Neural Networks Use Graphs When They Shouldn't. CoRR abs/2309.04332 (2023) - [i58]Roee Hendel, Mor Geva, Amir Globerson:
In-Context Learning Creates Task Vectors. CoRR abs/2310.15916 (2023) - 2022
- [c99]Edo Cohen-Karlik, Avichai Ben David, Nadav Cohen, Amir Globerson:
On the Implicit Bias of Gradient Descent for Temporal Extrapolation. AISTATS 2022: 10966-10981 - [c98]Roei Herzig, Elad Ben-Avraham, Karttikeya Mangalam, Amir Bar, Gal Chechik, Anna Rohrbach, Trevor Darrell, Amir Globerson:
Object-Region Video Transformers. CVPR 2022: 3138-3149 - [c97]Amir Bar, Xin Wang, Vadim Kantorov, Colorado J. Reed, Roei Herzig, Gal Chechik, Anna Rohrbach, Trevor Darrell, Amir Globerson:
DETReg: Unsupervised Pretraining with Region Priors for Object Detection. CVPR 2022: 14585-14595 - [c96]David Nukrai, Ron Mokady, Amir Globerson:
Text-Only Training for Image Captioning using Noise-Injected CLIP. EMNLP (Findings) 2022: 4055-4063 - [c95]Alon Brutzkus, Amir Globerson, Eran Malach, Alon Regev Netser, Shai Shalev-Shwartz:
Efficient Learning of CNNs using Patch Based Features. ICML 2022: 2336-2356 - [c94]Ori Ram, Gal Shachaf, Omer Levy, Jonathan Berant, Amir Globerson:
Learning to Retrieve Passages without Supervision. NAACL-HLT 2022: 2687-2700 - [c93]Amir Bar, Yossi Gandelsman, Trevor Darrell, Amir Globerson, Alexei A. Efros:
Visual Prompting via Image Inpainting. NeurIPS 2022 - [c92]Elad Ben-Avraham, Roei Herzig, Karttikeya Mangalam, Amir Bar, Anna Rohrbach, Leonid Karlinsky, Trevor Darrell, Amir Globerson:
Bringing Image Scene Structure to Video via Frame-Clip Consistency of Object Tokens. NeurIPS 2022 - [c91]Ido Bronstein, Alon Brutzkus, Amir Globerson:
On the inductive bias of neural networks for learning read-once DNFs. UAI 2022: 255-265 - [c90]Gal Yona, Shay Moran, Gal Elidan, Amir Globerson:
Active learning with label comparisons. UAI 2022: 2289-2298 - [i57]Edo Cohen-Karlik, Avichai Ben David, Nadav Cohen, Amir Globerson:
On the Implicit Bias of Gradient Descent for Temporal Extrapolation. CoRR abs/2202.04302 (2022) - [i56]Gal Yona, Shay Moran, Gal Elidan, Amir Globerson:
Active Learning with Label Comparisons. CoRR abs/2204.04670 (2022) - [i55]Elad Ben-Avraham, Roei Herzig, Karttikeya Mangalam, Amir Bar, Anna Rohrbach, Leonid Karlinsky, Trevor Darrell, Amir Globerson:
Bringing Image Scene Structure to Video via Frame-Clip Consistency of Object Tokens. CoRR abs/2206.06346 (2022) - [i54]Elad Ben-Avraham, Roei Herzig, Karttikeya Mangalam, Amir Bar, Anna Rohrbach, Leonid Karlinsky, Trevor Darrell, Amir Globerson:
Structured Video Tokens @ Ego4D PNR Temporal Localization Challenge 2022. CoRR abs/2206.07689 (2022) - [i53]Maya Bechler-Speicher, Amir Globerson, Ran Gilad-Bachrach:
Graph Trees with Attention. CoRR abs/2207.02760 (2022) - [i52]Amir Bar, Yossi Gandelsman, Trevor Darrell, Amir Globerson, Alexei A. Efros:
Visual Prompting via Image Inpainting. CoRR abs/2209.00647 (2022) - [i51]Edo Cohen-Karlik, Itamar Menuhin-Gruman, Nadav Cohen, Raja Giryes, Amir Globerson:
Learning Low Dimensional State Spaces with Overparameterized Recurrent Neural Network. CoRR abs/2210.14064 (2022) - [i50]David Nukrai, Ron Mokady, Amir Globerson:
Text-Only Training for Image Captioning using Noise-Injected CLIP. CoRR abs/2211.00575 (2022) - [i49]Roei Herzig, Ofir Abramovich, Elad Ben-Avraham, Assaf Arbelle, Leonid Karlinsky, Ariel Shamir, Trevor Darrell, Amir Globerson:
PromptonomyViT: Multi-Task Prompt Learning Improves Video Transformers using Synthetic Scene Data. CoRR abs/2212.04821 (2022) - [i48]Ori Ram, Liat Bezalel, Adi Zicher, Yonatan Belinkov, Jonathan Berant, Amir Globerson:
What Are You Token About? Dense Retrieval as Distributions Over the Vocabulary. CoRR abs/2212.10380 (2022) - 2021
- [c89]Ori Ram, Yuval Kirstain, Jonathan Berant, Amir Globerson, Omer Levy:
Few-Shot Question Answering by Pretraining Span Selection. ACL/IJCNLP (1) 2021: 3066-3079 - [c88]Adi Haviv, Jonathan Berant, Amir Globerson:
BERTese: Learning to Speak to BERT. EACL 2021: 3618-3623 - [c87]Oran Lang, Yossi Gandelsman, Michal Yarom, Yoav Wald, Gal Elidan, Avinatan Hassidim, William T. Freeman, Phillip Isola, Amir Globerson, Michal Irani, Inbar Mosseri:
Explaining in Style: Training a GAN to explain a classifier in StyleSpace. ICCV 2021: 673-682 - [c86]Shahar Azulay, Edward Moroshko, Mor Shpigel Nacson, Blake E. Woodworth, Nathan Srebro, Amir Globerson, Daniel Soudry:
On the Implicit Bias of Initialization Shape: Beyond Infinitesimal Mirror Descent. ICML 2021: 468-477 - [c85]Amir Bar, Roei Herzig, Xiaolong Wang, Anna Rohrbach, Gal Chechik, Trevor Darrell, Amir Globerson:
Compositional Video Synthesis with Action Graphs. ICML 2021: 662-673 - [c84]Roei Sarussi, Alon Brutzkus, Amir Globerson:
Towards Understanding Learning in Neural Networks with Linear Teachers. ICML 2021: 9313-9322 - [c83]Gal Shachaf, Alon Brutzkus, Amir Globerson:
A Theoretical Analysis of Fine-tuning with Linear Teachers. NeurIPS 2021: 15382-15394 - [c82]Alon Brutzkus, Amir Globerson:
An optimization and generalization analysis for max-pooling networks. UAI 2021: 1650-1660 - [i47]Ori Ram, Yuval Kirstain, Jonathan Berant, Amir Globerson, Omer Levy:
Few-Shot Question Answering by Pretraining Span Selection. CoRR abs/2101.00438 (2021) - [i46]Roei Sarussi, Alon Brutzkus, Amir Globerson:
Towards Understanding Learning in Neural Networks with Linear Teachers. CoRR abs/2101.02533 (2021) - [i45]Shahar Azulay, Edward Moroshko, Mor Shpigel Nacson, Blake E. Woodworth, Nathan Srebro, Amir Globerson, Daniel Soudry:
On the Implicit Bias of Initialization Shape: Beyond Infinitesimal Mirror Descent. CoRR abs/2102.09769 (2021) - [i44]Adi Haviv, Jonathan Berant, Amir Globerson:
BERTese: Learning to Speak to BERT. CoRR abs/2103.05327 (2021) - [i43]Oran Lang, Yossi Gandelsman, Michal Yarom, Yoav Wald, Gal Elidan, Avinatan Hassidim, William T. Freeman, Phillip Isola, Amir Globerson, Michal Irani, Inbar Mosseri:
Explaining in Style: Training a GAN to explain a classifier in StyleSpace. CoRR abs/2104.13369 (2021) - [i42]Amir Bar, Xin Wang, Vadim Kantorov, Colorado J. Reed, Roei Herzig, Gal Chechik, Anna Rohrbach, Trevor Darrell, Amir Globerson:
DETReg: Unsupervised Pretraining with Region Priors for Object Detection. CoRR abs/2106.04550 (2021) - [i41]Gal Shachaf, Alon Brutzkus, Amir Globerson:
A Theoretical Analysis of Fine-tuning with Linear Teachers. CoRR abs/2107.01641 (2021) - [i40]Roei Herzig, Elad Ben-Avraham, Karttikeya Mangalam, Amir Bar, Gal Chechik, Anna Rohrbach, Trevor Darrell, Amir Globerson:
Object-Region Video Transformers. CoRR abs/2110.06915 (2021) - [i39]Alon Itai, Amir Globerson, Ami Wiesel:
On the Optimization Landscape of Maximum Mean Discrepancy. CoRR abs/2110.13452 (2021) - [i38]Ori Ram, Gal Shachaf, Omer Levy, Jonathan Berant, Amir Globerson:
Learning to Retrieve Passages without Supervision. CoRR abs/2112.07708 (2021) - 2020
- [c81]Aviv Shamsian, Ofri Kleinfeld, Amir Globerson, Gal Chechik:
Learning Object Permanence from Video. ECCV (16) 2020: 35-50 - [c80]Roei Herzig, Amir Bar, Huijuan Xu, Gal Chechik, Trevor Darrell, Amir Globerson:
Learning Canonical Representations for Scene Graph to Image Generation. ECCV (26) 2020: 210-227 - [c79]Elad Segal, Avia Efrat, Mor Shoham, Amir Globerson, Jonathan Berant:
A Simple and Effective Model for Answering Multi-span Questions. EMNLP (1) 2020: 3074-3080 - [c78]Yuval Varkel, Amir Globerson:
Pre-training Mention Representations in Coreference Models. EMNLP (1) 2020: 8534-8540 - [c77]Roy Mor, Erez Peterfreund, Matan Gavish, Amir Globerson:
Optimal Strategies Against Generative Attacks. ICLR 2020 - [c76]Edo Cohen-Karlik, Avichai Ben David, Amir Globerson:
Regularizing Towards Permutation Invariance In Recurrent Models. NeurIPS 2020 - [c75]Moshiko Raboh, Roei Herzig, Jonathan Berant, Gal Chechik, Amir Globerson:
Differentiable Scene Graphs. WACV 2020: 1477-1486 - [i37]Alon Brutzkus, Amir Globerson:
On the Inductive Bias of a CNN for Orthogonal Patterns Distributions. CoRR abs/2002.09781 (2020) - [i36]Aviv Shamsian, Ofri Kleinfeld, Amir Globerson, Gal Chechik:
Learning Object Permanence from Video. CoRR abs/2003.10469 (2020) - [i35]Amir Bar, Roei Herzig, Xiaolong Wang, Gal Chechik, Trevor Darrell, Amir Globerson:
Compositional Video Synthesis with Action Graphs. CoRR abs/2006.15327 (2020) - [i34]Shahar Azulay, Lior Raz, Amir Globerson, Tomer Koren, Yehuda Afek:
Holdout SGD: Byzantine Tolerant Federated Learning. CoRR abs/2008.04612 (2020) - [i33]Achiya Jerbi, Roei Herzig, Jonathan Berant, Gal Chechik, Amir Globerson:
Learning Object Detection from Captions via Textual Scene Attributes. CoRR abs/2009.14558 (2020) - [i32]Nisan Chiprut, Amir Globerson, Ami Wiesel:
Maximin Optimization for Binary Regression. CoRR abs/2010.05077 (2020) - [i31]Edo Cohen-Karlik, Avichai Ben David, Amir Globerson:
Regularizing Towards Permutation Invariance in Recurrent Models. CoRR abs/2010.13055 (2020)
2010 – 2019
- 2019
- [c74]Ben Kantor, Amir Globerson:
Coreference Resolution with Entity Equalization. ACL (1) 2019: 673-677 - [c73]Deborah Cohen, Amit Daniely, Amir Globerson, Gal Elidan:
Learning Rules-First Classifiers. AISTATS 2019: 1398-1406 - [c72]Roei Herzig, Elad Levi, Huijuan Xu, Hang Gao, Eli Brosh, Xiaolong Wang, Amir Globerson, Trevor Darrell:
Spatio-Temporal Action Graph Networks. ICCV Workshops 2019: 2347-2356 - [c71]Jonathan Berant, Daniel Deutch, Amir Globerson, Tova Milo, Tomer Wolfson:
Explaining Queries Over Web Tables to Non-experts. ICDE 2019: 1570-1573 - [c70]Alon Brutzkus, Amir Globerson:
Why do Larger Models Generalize Better? A Theoretical Perspective via the XOR Problem. ICML 2019: 822-830 - [c69]Tal Schuster, Ori Ram, Regina Barzilay, Amir Globerson:
Cross-Lingual Alignment of Contextual Word Embeddings, with Applications to Zero-shot Dependency Parsing. NAACL-HLT (1) 2019: 1599-1613 - [e2]Amir Globerson, Ricardo Silva:
Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, UAI 2019, Tel Aviv, Israel, July 22-25, 2019. Proceedings of Machine Learning Research 115, AUAI Press 2019 [contents] - [i30]Tal Schuster, Ori Ram, Regina Barzilay, Amir Globerson:
Cross-Lingual Alignment of Contextual Word Embeddings, with Applications to Zero-shot Dependency Parsing. CoRR abs/1902.09492 (2019) - [i29]Moshiko Raboh, Roei Herzig, Gal Chechik, Jonathan Berant, Amir Globerson:
Learning Latent Scene-Graph Representations for Referring Relationships. CoRR abs/1902.10200 (2019) - [i28]Roei Herzig, Amir Bar, Huijuan Xu, Gal Chechik, Trevor Darrell, Amir Globerson:
Learning Canonical Representations for Scene Graph to Image Generation. CoRR abs/1912.07414 (2019) - 2018
- [c68]Omer Goldman, Veronica Latcinnik, Ehud Nave, Amir Globerson, Jonathan Berant:
Weakly Supervised Semantic Parsing with Abstract Examples. ACL (1) 2018: 1809-1819 - [c67]Nir Rosenfeld, Amir Globerson:
Semi-Supervised Learning with Competitive Infection Models. AISTATS 2018: 336-346 - [c66]Alon Brutzkus, Amir Globerson, Eran Malach, Shai Shalev-Shwartz:
SGD Learns Over-parameterized Networks that Provably Generalize on Linearly Separable Data. ICLR (Poster) 2018 - [c65]Nataly Brukhim, Amir Globerson:
Predict and Constrain: Modeling Cardinality in Deep Structured Prediction. ICML 2018: 658-666 - [c64]Nir Rosenfeld, Eric Balkanski, Amir Globerson, Yaron Singer:
Learning to Optimize Combinatorial Functions. ICML 2018: 4371-4380 - [c63]Roei Herzig, Moshiko Raboh, Gal Chechik, Jonathan Berant, Amir Globerson:
Mapping Images to Scene Graphs with Permutation-Invariant Structured Prediction. NeurIPS 2018: 7211-7221 - [e1]Amir Globerson, Ricardo Silva:
Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence, UAI 2018, Monterey, California, USA, August 6-10, 2018. AUAI Press 2018 [contents] - [i27]Nataly Brukhim, Amir Globerson:
Predict and Constrain: Modeling Cardinality in Deep Structured Prediction. CoRR abs/1802.04721 (2018) - [i26]Roei Herzig, Moshiko Raboh, Gal Chechik, Jonathan Berant, Amir Globerson:
Mapping Images to Scene Graphs with Permutation-Invariant Structured Prediction. CoRR abs/1802.05451 (2018) - [i25]Deborah Cohen, Amit Daniely, Amir Globerson, Gal Elidan:
Learning with Rules. CoRR abs/1803.03155 (2018) - [i24]Jonathan Berant, Daniel Deutch, Amir Globerson, Tova Milo, Tomer Wolfson:
Explaining Queries over Web Tables to Non-Experts. CoRR abs/1808.04614 (2018) - [i23]Alon Brutzkus, Amir Globerson:
Over-parameterization Improves Generalization in the XOR Detection Problem. CoRR abs/1810.03037 (2018) - [i22]Roei Herzig, Elad Levi, Huijuan Xu, Eli Brosh, Amir Globerson, Trevor Darrell:
Classifying Collisions with Spatio-Temporal Action Graph Networks. CoRR abs/1812.01233 (2018) - 2017
- [c62]Amir Globerson, Roi Livni, Shai Shalev-Shwartz:
Effective Semisupervised Learning on Manifolds. COLT 2017: 978-1003 - [c61]Alon Brutzkus, Amir Globerson:
Globally Optimal Gradient Descent for a ConvNet with Gaussian Inputs. ICML 2017: 605-614 - [c60]Roi Livni, Daniel Carmon, Amir Globerson:
Learning Infinite Layer Networks Without the Kernel Trick. ICML 2017: 2198-2207 - [c59]Yoav Wald, Amir Globerson:
Robust Conditional Probabilities. NIPS 2017: 6359-6368 - [c58]Amir Globerson:
Learning and Inference with Expectations. UAI 2017 - [i21]Alon Brutzkus, Amir Globerson:
Globally Optimal Gradient Descent for a ConvNet with Gaussian Inputs. CoRR abs/1702.07966 (2017) - [i20]Nir Rosenfeld, Amir Globerson:
Semi-Supervised Learning with Competitive Infection Models. CoRR abs/1703.06426 (2017) - [i19]Yoav Wald, Amir Globerson:
Robust Conditional Probabilities. CoRR abs/1708.02406 (2017) - [i18]Alon Brutzkus, Amir Globerson, Eran Malach, Shai Shalev-Shwartz:
SGD Learns Over-parameterized Networks that Provably Generalize on Linearly Separable Data. CoRR abs/1710.10174 (2017) - [i17]Omer Goldman, Veronica Latcinnik, Udi Naveh, Amir Globerson, Jonathan Berant:
Weakly-supervised Semantic Parsing with Abstract Examples. CoRR abs/1711.05240 (2017) - 2016
- [c57]Amir Globerson, Nevena Lazic, Soumen Chakrabarti, Amarnag Subramanya, Michael Ringgaard, Fernando Pereira:
Collective Entity Resolution with Multi-Focal Attention. ACL (1) 2016 - [c56]Uri Heinemann, Roi Livni, Elad Eban, Gal Elidan, Amir Globerson:
Improper Deep Kernels. AISTATS 2016: 1159-1167 - [c55]Nir Rosenfeld, Amir Globerson:
Optimal Tagging with Markov Chain Optimization. NIPS 2016: 1307-1315 - [c54]Nir Rosenfeld, Mor Nitzan, Amir Globerson:
Discriminative Learning of Infection Models. WSDM 2016: 563-572 - [i16]Nir Rosenfeld, Amir Globerson:
Optimal Tagging with Markov Chain Optimization. CoRR abs/1605.04719 (2016) - [i15]Amir Globerson, Roi Livni:
Learning Infinite-Layer Networks: Beyond the Kernel Trick. CoRR abs/1606.05316 (2016) - [i14]Yuval Atzmon, Jonathan Berant, Vahid Kezami, Amir Globerson, Gal Chechik:
Learning to generalize to new compositions in image understanding. CoRR abs/1608.07639 (2016) - 2015
- [j8]Yonatan Belinkov, Tao Lei, Regina Barzilay, Amir Globerson:
Erratum: "Exploring Compositional Architectures and Word Vector Representations for Prepositional Phrase Attachment". Trans. Assoc. Comput. Linguistics 3: 101 (2015) - [c53]Amir Globerson, Tim Roughgarden, David A. Sontag, Cafer Yildirim:
How Hard is Inference for Structured Prediction? ICML 2015: 2181-2190 - [c52]Hillel Taub-Tabib, Yoav Goldberg, Amir Globerson:
Template Kernels for Dependency Parsing. HLT-NAACL 2015: 1422-1427 - 2014
- [j7]Yonatan Belinkov, Tao Lei, Regina Barzilay, Amir Globerson:
Exploring Compositional Architectures and Word Vector Representations for Prepositional Phrase Attachment. Trans. Assoc. Comput. Linguistics 2: 561-572 (2014) - [c51]Yuan Zhang, Tao Lei, Regina Barzilay, Tommi S. Jaakkola, Amir Globerson:
Steps to Excellence: Simple Inference with Refined Scoring of Dependency Trees. ACL (1) 2014: 197-207 - [c50]Martin Mladenov, Kristian Kersting, Amir Globerson:
Efficient Lifting of MAP LP Relaxations Using k-Locality. AISTATS 2014: 623-632 - [c49]Nir Rosenfeld, Ofer Meshi, Daniel Tarlow, Amir Globerson:
Learning Structured Models with the AUC Loss and Its Generalizations. AISTATS 2014: 841-849 - [c48]Elad Eban, Elad Mezuman, Amir Globerson:
Discrete Chebyshev Classifiers. ICML 2014: 1233-1241 - [c47]Uri Heinemann, Amir Globerson:
Inferning with High Girth Graphical Models. ICML 2014: 1260-1268 - [c46]Ariadna Quattoni, Borja Balle, Xavier Carreras, Amir Globerson:
Spectral Regularization for Max-Margin Sequence Tagging. ICML 2014: 1710-1718 - [c45]Martin Mladenov, Amir Globerson, Kristian Kersting:
Lifted Message Passing as Reparametrization of Graphical Models. UAI 2014: 603-612 - [c44]Yoav Wald, Amir Globerson:
Tightness Results for Local Consistency Relaxations in Continuous MRFs. UAI 2014: 839-848 - [i13]Amir Globerson, Tim Roughgarden, David A. Sontag, Cafer Yildirim:
Tight Error Bounds for Structured Prediction. CoRR abs/1409.5834 (2014) - 2013
- [j6]Ami Wiesel, Ofir Bibi, Amir Globerson:
Time Varying Autoregressive Moving Average Models for Covariance Estimation. IEEE Trans. Signal Process. 61(11): 2791-2801 (2013) - [c43]Yuan Zhang, Regina Barzilay, Amir Globerson:
Transfer Learning for Constituency-Based Grammars. ACL (1) 2013: 291-301 - [c42]Chetan Arora, Amir Globerson:
Higher Order Matching for Consistent Multiple Target Tracking. ICCV 2013: 177-184 - [c41]Ofir Pele, Ben Taskar, Amir Globerson, Michael Werman:
The Pairwise Piecewise-Linear Embedding for Efficient Non-Linear Classification. ICML (1) 2013: 205-213 - [c40]Roi Livni, David Lehavi, Sagi Schein, Hila Nachlieli, Shai Shalev-Shwartz, Amir Globerson:
Vanishing Component Analysis. ICML (1) 2013: 597-605 - [c39]Ofer Meshi, Elad Eban, Gal Elidan, Amir Globerson:
Learning Max-Margin Tree Predictors. UAI 2013 - [c38]Elad Mezuman, Daniel Tarlow, Amir Globerson, Yair Weiss:
Tighter Linear Program Relaxations for High Order Graphical Models. UAI 2013 - [i12]Ofer Meshi, Elad Eban, Gal Elidan, Amir Globerson:
Learning Max-Margin Tree Predictors. CoRR abs/1309.6847 (2013) - [i11]Elad Mezuman, Daniel Tarlow, Amir Globerson, Yair Weiss:
Tighter Linear Program Relaxations for High Order Graphical Models. CoRR abs/1309.6848 (2013) - 2012
- [c37]Tahira Naseem, Regina Barzilay, Amir Globerson:
Selective Sharing for Multilingual Dependency Parsing. ACL (1) 2012: 629-637 - [c36]Yuan Zhang, Roi Reichart, Regina Barzilay, Amir Globerson:
Learning to Map into a Universal POS Tagset. EMNLP-CoNLL 2012: 1368-1378 - [c35]Alexander M. Rush, Roi Reichart, Michael Collins, Amir Globerson:
Improved Parsing and POS Tagging Using Inter-Sentence Consistency Constraints. EMNLP-CoNLL 2012: 1434-1444 - [c34]Elad Eban, Aharon Birnbaum, Shai Shalev-Shwartz, Amir Globerson:
Learning the Experts for Online Sequence Prediction. ICML 2012 - [c33]Ami Wiesel, Amir Globerson:
Covariance estimation in time varying ARMA processes. SAM 2012: 357-360 - [c32]Ofer Meshi, Tommi S. Jaakkola, Amir Globerson:
Convergence Rate Analysis of MAP Coordinate Minimization Algorithms. NIPS 2012: 3023-3031 - [c31]Roi Livni, Koby Crammer, Amir Globerson:
A Simple Geometric Interpretation of SVM using Stochastic Adversaries. AISTATS 2012: 722-730 - [i10]Uri Heinemann, Amir Globerson:
What Cannot be Learned with Bethe Approximations. CoRR abs/1202.3731 (2012) - [i9]Ofer Meshi, Ariel Jaimovich, Amir Globerson, Nir Friedman:
Convexifying the Bethe Free Energy. CoRR abs/1205.2624 (2012) - [i8]Talya Meltzer, Amir Globerson, Yair Weiss:
Convergent message passing algorithms - a unifying view. CoRR abs/1205.2625 (2012) - [i7]David A. Sontag, Talya Meltzer, Amir Globerson, Tommi S. Jaakkola, Yair Weiss:
Tightening LP Relaxations for MAP using Message Passing. CoRR abs/1206.3288 (2012) - [i6]Elad Eban, Aharon Birnbaum, Shai Shalev-Shwartz, Amir Globerson:
Learning the Experts for Online Sequence Prediction. CoRR abs/1206.4604 (2012) - [i5]Amir Globerson, Tommi S. Jaakkola:
Convergent Propagation Algorithms via Oriented Trees. CoRR abs/1206.5243 (2012) - [i4]Koby Crammer, Amir Globerson:
Discriminative Learning via Semidefinite Probabilistic Models. CoRR abs/1206.6815 (2012) - [i3]Amir Globerson, Naftali Tishby:
The Minimum Information Principle for Discriminative Learning. CoRR abs/1207.4110 (2012) - [i2]Amir Globerson, Gal Chechik, Naftali Tishby:
Sufficient Dimensionality Reduction with Irrelevant Statistics. CoRR abs/1212.2483 (2012) - 2011
- [c30]Ofer Meshi, Amir Globerson:
An Alternating Direction Method for Dual MAP LP Relaxation. ECML/PKDD (2) 2011: 470-483 - [c29]Uri Heinemann, Amir Globerson:
What Cannot be Learned with Bethe Approximations. UAI 2011: 319-326 - [i1]Ido Ginodi, Amir Globerson:
Gaussian Robust Classification. CoRR abs/1104.0235 (2011) - 2010
- [c28]Ofer Meshi, David A. Sontag, Tommi S. Jaakkola, Amir Globerson:
Learning Efficiently with Approximate Inference via Dual Losses. ICML 2010: 783-790 - [c27]David A. Sontag, Ofer Meshi, Tommi S. Jaakkola, Amir Globerson:
More data means less inference: A pseudo-max approach to structured learning. NIPS 2010: 2181-2189 - [c26]Tommi S. Jaakkola, David A. Sontag, Amir Globerson, Marina Meila:
Learning Bayesian Network Structure using LP Relaxations. AISTATS 2010: 358-365
2000 – 2009
- 2009
- [j5]Amir Globerson, Eran Stark, Eilon Vaadia, Naftali Tishby:
The minimum information principle and its application to neural code analysis. Proc. Natl. Acad. Sci. USA 106(9): 3490-3495 (2009) - [c25]Menachem Fromer, Amir Globerson:
An LP View of the M-best MAP problem. NIPS 2009: 567-575 - [c24]Talya Meltzer, Amir Globerson, Yair Weiss:
Convergent message passing algorithms - a unifying view. UAI 2009: 393-401 - [c23]Ofer Meshi, Ariel Jaimovich, Amir Globerson, Nir Friedman:
Convexifying the Bethe Free Energy. UAI 2009: 402-410 - 2008
- [j4]Michael Collins, Amir Globerson, Terry Koo, Xavier Carreras, Peter L. Bartlett:
Exponentiated Gradient Algorithms for Conditional Random Fields and Max-Margin Markov Networks. J. Mach. Learn. Res. 9: 1775-1822 (2008) - [c22]David A. Sontag, Amir Globerson, Tommi S. Jaakkola:
Clusters and Coarse Partitions in LP Relaxations. NIPS 2008: 1537-1544 - [c21]David A. Sontag, Talya Meltzer, Amir Globerson, Tommi S. Jaakkola, Yair Weiss:
Tightening LP Relaxations for MAP using Message Passing. UAI 2008: 503-510 - 2007
- [j3]Amir Globerson, Gal Chechik, Fernando Pereira, Naftali Tishby:
Euclidean Embedding of Co-occurrence Data. J. Mach. Learn. Res. 8: 2265-2295 (2007) - [c20]Terry Koo, Amir Globerson, Xavier Carreras, Michael Collins:
Structured Prediction Models via the Matrix-Tree Theorem. EMNLP-CoNLL 2007: 141-150 - [c19]Amir Globerson, Terry Koo, Xavier Carreras, Michael Collins:
Exponentiated gradient algorithms for log-linear structured prediction. ICML 2007: 305-312 - [c18]Amir Globerson, Tommi S. Jaakkola:
Fixing Max-Product: Convergent Message Passing Algorithms for MAP LP-Relaxations. NIPS 2007: 553-560 - [c17]Choon Hui Teo, Amir Globerson, Sam T. Roweis, Alexander J. Smola:
Convex Learning with Invariances. NIPS 2007: 1489-1496 - [c16]Amir Globerson, Tommi S. Jaakkola:
Convergent Propagation Algorithms via Oriented Trees. UAI 2007: 133-140 - [c15]Amir Globerson, Tommi S. Jaakkola:
Approximate inference using conditional entropy decompositions. AISTATS 2007: 130-138 - [c14]Amir Globerson, Sam T. Roweis:
Visualizing pairwise similarity via semidefinite programming. AISTATS 2007: 139-146 - 2006
- [c13]Amir Globerson, Gal Chechik, Fernando Pereira, Naftali Tishby:
Embedding Heterogeneous Data Using Statistical Models. AAAI 2006: 1605-1608 - [c12]Amir Globerson, Sam T. Roweis:
Nightmare at test time: robust learning by feature deletion. ICML 2006: 353-360 - [c11]Amir Globerson, Tommi S. Jaakkola:
Approximate inference using planar graph decomposition. NIPS 2006: 473-480 - [c10]Koby Crammer, Amir Globerson:
Discriminative Learning via Semidefinite Probabilistic Models. UAI 2006 - 2005
- [j2]Gal Chechik, Amir Globerson, Naftali Tishby, Yair Weiss:
Information Bottleneck for Gaussian Variables. J. Mach. Learn. Res. 6: 165-188 (2005) - [c9]John Blitzer, Amir Globerson, Fernando Pereira:
Distributed Latent Variable Models of Lexical Co-occurrences. AISTATS 2005: 25-32 - [c8]Amir Globerson, Sam T. Roweis:
Metric Learning by Collapsing Classes. NIPS 2005: 451-458 - 2004
- [c7]Amir Globerson, Gal Chechik, Fernando C. N. Pereira, Naftali Tishby:
Euclidean Embedding of Co-Occurrence Data. NIPS 2004: 497-504 - [c6]Amir Globerson, Naftali Tishby:
The Minimum Information Principle for Discriminative Learning. UAI 2004: 193-200 - 2003
- [j1]Amir Globerson, Naftali Tishby:
Sufficient Dimensionality Reduction. J. Mach. Learn. Res. 3: 1307-1331 (2003) - [c5]Gal Chechik, Amir Globerson, Naftali Tishby, Yair Weiss:
Information Bottleneck for Gaussian Variables. NIPS 2003: 1213-1220 - [c4]Amir Globerson, Gal Chechik, Naftali Tishby:
Sufficient Dimensionality Reduction with Irrelevance Statistics. UAI 2003: 281-288 - 2002
- [c3]Amir Globerson, Naftali Tishby:
Most Informative Dimension Reduction. AAAI/IAAI 2002: 1024- - [c2]Amir Globerson, Naftali Tishby:
Sufficient Dimensionality Reduction - A novel Analysis Method. ICML 2002: 203-210 - 2001
- [c1]Gal Chechik, Amir Globerson, Michael J. Anderson, Eric D. Young, Israel Nelken, Naftali Tishby:
Group Redundancy Measures Reveal Redundancy Reduction in the Auditory Pathway. NIPS 2001: 173-180
Coauthor Index
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